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Home » NFL Football » 2013 Week 7 Free NFL Picks for Sunday, 10/20
2013 Week 7 Free NFL Picks for Sunday, 10/20
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2013 Sunday Free Week 7 NFL Football Picks Below
Last Week's (Week Six) NFL Picks Results: 7-5 (58%)
2013 NFL Regular Season NFL Picks Results: 57-53-2 (51%)
Date and Time: Sunday 10/20/13 at 1:00pm on FOX
Game Line: Philadelphia Eagles -2.5 (from 5Dimes)
Over/Under: 54.5
Preview: The Cowboys and Eagles are facing off to determine who will be the early season first place team, and team to beat in the NFC East. The Eagles offense is still putting up big numbers to the tune of 28 points and 450 yards per game and they lead the NFL in rushing averaging 178 yards per game. Michael Vick missed last weeks game and he is questionable again this week but backup quarterback Nick Foles has proven he can lead this team just as well, if not better by limiting the turnovers. They will try to continue to spread it around, with LeSean McCoy, DeSean Jackson and Riley Cooper all catching their fair share of passes and touchdowns regardless of who is leading the offense. Defensively, the Eagles are going to play an extremely one dimensional passing attack with Dallas. The Cowboys are playing solid football but it hasn't given them the results in the win column they've hoped for. Their offense is averaging 30 points per game, second best in the NFL, and Tony Romo has looked outstanding completing 70% of his passes with 14 touchdowns and only 3 interceptions. DeMarco Murry was controlling the Cowboys rushing attack all by himself but he was injured last week and is expected to miss 3-6 weeks, leaving the rushing duties to Joseph Randle, a rookie who looked decent in relief of Murray last week. The Cowboys are also entering this game without their best defensive player, DeMarcus Ware, who will also miss extended time with an injury. While neither of these teams are among the NFL's elite it is nice to see them face off in a meaningful game for a change. The big advantage the Eagles offense has here is in the running game. They are the best in the NFL, while Dallas is only middle of the pack and will have to go without their starting RB. If the Eagles can control the clock with the running game and keep Romo off the field they should find some success. Neither team has lit it up against the spread and neither will be able to stop the other offensively in what should be a shootout, we will take the home team that is coming in more healthy, with more offensive weapons to play with.
Cowboys at Eagles Free NFL Pick: Eagles -2.5 and over 54.5
Cowboys at Eagles Game Prediction: 31-26 Eagles
What to expect in this week 7 NFL matchup: Whether it is Nick Foles or Michael Vick at quarterback for Philadelphia, they will run the ball a ton with LeSean McCoy against a defense that struggles against the run and will be without their star defensive lineman. This should set up the play action pass and the fast tempo should force the Cowboys secondary to make a few mistakes. Dallas shouldn't have any troubles moving the ball offensively either, but the injury to DeMarco Murray will be too much, and force them to be too one dimensional to win a divisional game on the road.
Tampa Bay Buccaneers at Atlanta Falcons
Game Line: Atlanta Falcons -7 (from 5Dimes)
Over/Under: 43
Preview: The Atlanta Falcons suffered the worst injury in the league this season when star wide receiver Julio Jones went down for the season with a foot injury. Meanwhile, they have plenty of other injury concerns, fellow receiver Roddy White is questionable with hamstring and ankle problems and their big off season pick up RB Stephen Jackson has missed three straight games. Thankfully, they are coming off a bye week, giving them extra time to prepare for these losses when they take on divisional foe in Tampa Bay. If Atlanta is going to be successful they're going to need Roddy White to be far more effective, he has been a non-factor for the Falcons so far in 2013 and veteran tight end Tony Gonzalez is going to have to stay healthy and continue producing. The Tampa Bay Buccaneers are winless, losing three games by 3 points or fewer. They're relying on rookie quarterback Mike Glennon making his second start. He played well for three quarters against Philadelphia last week, trailing 21-20 after three quarters before letting the Eagles pull away. If they are going to have a chance in this game they're going to need a big performance from RB Doug Martin, something they haven't gotten the last three weeks. He has been a big let down and if this team is going to start winning games with Glennon at the helm he is going to have to be more effective. Atlanta allows less than 100 yards per game on the ground so this won't be an easy task. Without running the ball successfully Tampa doesn't have the weapons on the outside to score enough to keep up with the Falcons. Regardless of who Matt Ryan is throwing the ball to, they're going to find their way down the field against a Bucs defense that has been surprisingly beatable. Tampa Bay is giving up 250 yards per game through the air, a much higher average than most anticipated with their strong secondary. The Buccaneers are weakest in the linebacking core, which is the only area of the field Matt Ryan should be able to exploit with Tony Gonzalez and Harry Douglas.
Buccaneers at Falcons Free NFL Pick: Atlanta Falcons -7 and under 43
Buccaneers at Falcons Game Prediction: 24-14 Falcons
What to expect in this week 7 NFL matchup: Mike Glennon looks more like a rookie than he did in his first start. The Falcons have a bye week to prepare their famous exotic blitz packages and scheme changes to beat the young quarterback to force some turnovers and end drives quickly on third down. Offensively, the big play potential for Atlanta is gone, but they should find success dink-and-dunking down the field with quick short passes. If Stephen Jackson returns as scheduled he will make a huge difference reducing the pressure on Matt Ryan and the Falcons win a game they have to have at home.
Chicago Bears at Washington Redskins
Game Line: Washington Redskins -1 (from 5Dimes)
Preview: The Washington Redskins have been one of the biggest letdown teams in the league this season. They're sitting at 1-4 and hoping to avoid going 0-3 at home for the first time in 15 years. They've found a way to average 400 yards per game, fourth in the league, while only scoring 21 points, ranking 20th. Combine that with one of the leagues worst defenses and a quarterback in a sophomore or post injury slump and you've got a recipe for disaster in the nation's capital. On top of that, they're last in the league in special teams, which could play a key factor against Devin Hester and the Chicago special teams that ranks among the best in the league. Robert Griffith III has really been struggling, whether it be the knee injury or opposing defenses figuring him out, he has to play better if this team is going to get back in the playoff picture in the NFC East. His 58% completion percentage and 6/5 touchdown to turnover ratio are among the worst in the NFL. However, the Redskins have found a way to win four straight against Chicago and DeAngelo Hall loves to play against Jay Cutler, with four picks in those four games. Cutler has been more effective this year with the emergence of WR Alshon Jeffery and TE Martellus Bennett but he is still prone to a costly interception with six through six games. They should have no problem moving the ball against the Washington secondary if Cutler can keep the turnovers to a minimum. The Bears defensive front has been anything but Bears like through six games. They're 30th in the league in sacks and lost two big tackles in Henry Melton and Nate Collins to season ending injuries. They gave up 100 yards and a touchdown to Brandon Jacobs and the Giants last week. This bodes well for a run heavy Washington offense. Both teams will be getting their share of points in this contest, where both teams have gone OVER the total frequently already this season.
Bears at Redskins Free NFL Picks: Redskins -1 and over 50
Bears at Redskins Game Prediction: 31-27 Redskins
What to expect in this week 7 NFL matchup: Defense will be a hard thing to come by in this contest. Washington will pound the ball with success against a beat up Bears defensive front, which will help limit the turnovers from Griffith and keep their bad defense off the field. Assuming Jay Cutler doesn't go turnover happy, the Bears will go pass happy against a bottom tier passing defense and Brandon Marshall and Alshon Jeffery will both have big games and put up points too, but in a battle of a pass happy road team and a run happy home team we will take the Redskins ice the game in the fourth quarter.
San Diego Chargers at Jacksonville Jaguars
Date and Time: Sunday 10/10/13 at 1:00pm on CBS
Game Line: San Diego Chargers -7.5 (from 5Dimes)
Preview: When the San Diego Chargers travel East to play at Jacksonville this week we will see two of the leagues premiere young emerging wide receivers. Rookie Keenan Allen for the Chargers has gone over 100 yards two games in a row with a touchdown and great looking hands over the middle and Jacksonville's Justin Blackmon is finally on the field after a suspension and had 14 catches for 190 yards against the Denver defense last week. As far as 0-7 teams go, Jacksonville has played much better in their last two games with Blackmon back and they're coming home to face a Chargers team that just played on Monday Night Football in a big win against Indianapolis. These are two of the leagues worst defenses in terms of yards, both over 380 so far this season. The difference in this game is going to be Jacksonville's total lack of running game, which will be what plagues them all season long. Maurice Jones-Drew is a shell of him former self and appears to have checked out both mentally and physically and you just can't rely on Chad Henne or Blaine Gabbert to win games in the NFL. Meanwhile, San Diego RB Ryan Matthews went over 100 yards for the first time this season on Monday, and looked the best he has since coming into the league. Their running game will control the clock and be effective, but the Jaguars will score on a few big plays and if Jones-Drew decided to come to the party offensively this team could score enough to compete at home here. The Chargers run defense is just average in the league, and an effective run game is as much motivation as anything else for someone in Jones-Drew who has proven they can be a top back in the league. Chargers QB Philip Rivers has owned Jacksonville since coming into the league and there's no reason that won't continue here, but Jacksonville's offense has a new found big play potential and they should find the endzone a few times to keep this game within the number in a higher scoring game that people think.
Chargers at Jaguars Free NFL Picks: Jaguars +7.5 and over 45
Chargers at Jaguars Game Prediction: 29-24 Chargers
What to expect in this week 7 NFL matchup: The Chargers will have no problem running the ball, like any other team that faces the Jacksonville run defense. Ryan Matthews and Danny Woodhead will look like elite NFL backs and Philip Rivers will manage the game well completing a high percentage of short range passes. The ball possession offense form the Chargers won't go crazy against the Jacksonville defense. Offensively, the Jaguars will be boom or bust, scoring on big plays and long runs, or punting quickly. They will find enough big plays and enough of a running game to keep this one close with the Chargers in a let down spot, traveling, on a short week.
San Francisco 49ers at Tennessee Titans
Date and Time: Sunday 10/10/13 at 4:05 on Fox
Game Line: San Francisco 49ers -4 (from 5Dimes)
Preview: The San Francisco 49ers have found their running game lately, and that's bad news for opposing defenses. After struggling to get going early in the season the 49ers have won three in a row while rushing for over 180 yards per game in those wins. They are one of the best ball possession offenses in the NFL and they showed that in the fourth quarter against the Cardinals last week with a 9 minute touchdown drive to put the game away. Frank Gore is carrying a lot of the load, 5.7 yards per carry on 25 rushes last week, which is taking a lot of stress from QB Colin Kaepernick who has finally got it going with the run game as well. They've been relying heavily on two guys, Vernon Davis and Anquan Boldin in the passing game but Mario Manningham practiced this week for the first time this season and would be a huge addition to the San Francisco offense if he plays. Defensively, the Titans have a group that matches the 49ers in every statistical category, and they have the advantage of being at home for this one, but if they're going to win they're going to have to do better offensively. Without QB Jake Locker the Titans can't get anything going through the passing game. The Titans started 3-0 because they didn't turn the ball over but since Locker went down and Ryan Fitzpatrick started they're 0-2 with five turnovers in those two losses. Their offensive line play is solid but they have the worst receiving core in the league, led by Nate Washington and Kendal Wright. Without reliable options Fitzpatrick has no hope of being successful, which means Chris Johnson is going to need play a lot better. He is averaging a mere 3 yards per carry and hasn't had the big breakout run yet this season with zero touchdowns through six games. He looks just as unmotivated and sluggish as Maurice Jones-Drew and it is really hurting his team who has the defense and the offensive line to be a good team as they showed earlier this season. The Titans have been covering machines so far this season, and this number has us scratching our heads, we assumed the 49ers would be in the -6.5 neighborhood, and they're being bet heavily already this week. This one smells like a trap and definitely has the makings of a low scoring grinder. We will call Vegas' bluff and take the home dog in a slow paced, grind it out, run heavy, low scoring game.
49ers at Titans Free NFL Pick: Titans +4 and under 40
49ers at Titans Game Prediction: 19-17 49ers
What to expect in this week 7 NFL matchup: Both teams will be run heavy throughout the game, and both run defenses have been stout this season. There will be plenty of punting, field position battling, field goals and hard hits. Chris Johnson will have his first 100 yard game this season but will carry the ball 20+ times and the clock will be running constantly while Frank Gore gets his 20+ carries as well. Fitz Patrick will throw his team a few costly interceptions but the Titans defense will step up and hold San Francisco to field goals to keep this game low scoring and keep themselves within the number.
Denver Broncos at Indianapolis Colts
Date and Time: Sunday 10/20/13 at 8:30pm on NBC
Game Line: Denver Broncos -6 (from 5Dimes)
Preview: Lastly, the game that just can't get enough coverage on ESPN , Peyton Manning's triumphant return to Indianapolis to take on "new guy" Andrew Luck and the Colts. This game has no shortage of story lines, the Colts are honoring Peyton Manning before the game and their owner has already had a few quotes about Peyton's time in Indianapolis that are making headlines. The Colts will be the biggest challenge the Denver offense has faced so far this season, the Colts rank fifth in the NFL in pass defense and scoring defense. They're also going to be without starting right tackle Orlando Franklin with a knee injury, adding to their offensive line problems. For the Colts offense, this is the biggest game of the year for Andrew Luck. He wants to step up and compete and prove that he is the new horse in Indianapolis and is going to continue their 20 year run as a franchise. He should be successful against a Denver pass defense that really needs to get healthy before they're going to be effective. As a unit they allow over 400 yards per game, 335 through the air. Assuming Denver scores early, Indianapolis will be playing from behind and passing the ball often but they will stay in this game. This line has a lot of people really liking Denver, especially after seeing Indianapolis struggle offensively on Monday Night Football but a team is never as good or as bad as their last game. This is a great matchup for Indianapolis, their defensive secondary is good and their run defense is weak, but Denver doesn't run the ball. Offensively, the Colts like to throw it around, and defensively the Broncos shut down the run but can't stop the pass. Indianapolis should get up for this game as a team and they're going to surprise a lot of people when they hold the Denver offense under 30 and compete right down to the last play of the game. We see this one ending up a lot like the Dallas game for Denver, a last second Peyton Manning drive for a game winning field goal, except this game will only see about half the number of total points.
Broncos at Colts Free NFL Pick: Indianapolis Colts +6 and under 55.5
Broncos at Colts Game Prediction:
What to expect in this week 7 NFL matchup: The Colts defense will shock America with their success matching up man to man and competing with Peyton Manning and his band of pass catchers. They will be held under 35 points for the first time this season and will turn to RB Knowshon Moreno more than any game so far in 2013. Andrew Luck will be throwing the ball early and often, with all of the motivation a tandem can have, he and WR Reggie Wayne will connect for a few big scores and this game will be a close one throughout. Peyton will drive his team down late in the fourth quarter to give his team the win and the media will love every second of it.
MLB Free Picks: Tigers @ Red Sox ALCS Game 6 Preview 10/19/13
2013 World Series Game 1 Picks: Cardinals vs Red Sox Picks for 10/23
Tags: 2013football picksfree football picksFree NFL picksFree NFL PredictionsFree PicksNFL picksSunday PicksWeek 7
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NFL Football Power Rankings – Week 3 (as of 9/20)
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DirecTV Raises NFL Sunday Ticket Prices AGAIN in 2017
The Overall Quality of the NFL Today is Being Deflated
The New England Patriots: Looking to Make a Statement
Free Super Bowl XLIX Pick: New England Patriots vs. Seattle Seahawks
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BENJAMIN FRIEDMAN - DIRECTOR / PRODUCER
Reelz "Fortune Fights"
Ten episode documentary series for Reelz TV
https://www.reelz.com/fortune-fights/
About Fortune Fights
The saying more money more problems definitely rings true with some celebrity mega fortunes. Whether it's a pricey divorce, shocking inheritances for unprepared heirs or untimely deaths when a wealthy celebrity has a massive estate there's often an army of people nudging around for their cut of the pie.
In hour-long episodes of Famous Fortune Fights husband and wife media experts and attorneys Danielle and Andy Mayoras explore the legal ups and downs and fortunes earned and lost by some of America's biggest household names. Johnny Depp, Madonna, Britney Spears, Harrison Ford, Hulk Hogan, Donald Sterling, Casey Kasem, Lisa Marie Presley, Robin Williams, Sonny Bono are the focus of episodes that show viewers how they earned their millions, how they saved or squandered it and the lawsuits, divorces, family fights and bad business partners led to famous fortune fights.
Benjamin Friedman is a freelance director / producer of commercial, tv, documentary and narrative films.
© 2022 BENJAMIN FRIEDMAN - DIRECTOR / PRODUCER. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


text:
Goldman Sachs Greg Smith Resignation: 4 Other Controversies To Hit The Firm
By Amanda Remling @AmandaTVScoop
The resignation letter printed as an op-ed in The New York Times on Wednesday by Greg Smith, a London-based executive director for Goldman Sachs that oversees equity derivatives, has stirred up a lot of trouble for the firm. Smith claimed that the environment now is as toxic and destructive as I have ever seen it.
The former employee went on to state that The firm has veered so far from the place I joined right out of college that I can longer in good conscience say that I identify with what it stands for. Smith claims to have overheard employees talking about ripping off their clients, and referring to them as muppets.
This upset is far from the first for the firm. Goldman Sachs has been involved in a string of controversies ranging from involvement with Greek debt to insider trading.
Goldman Sachs Involvement In Greek Debt
In 2010 it was uncovered that Goldman Sachs helped to hide billions of dollars in debt from budget overseers in Brussels. The bankers had previously helped the Greek government obscure billions back in 2001. According to the New York Times, in order to meet Europe's deficit rules, Goldman made a deal with Athens that allowed spending to continue. In 2001 Goldman made a currency trade which resulted in the sale of $15 billion (£9.8 billion) of Greek bonds. The trade allowed the government to mask the true extent of its budget deficit, wrote the UK Telegraph.
Goldman Sachs, among other American groups, was blamed for having a hand in running-up Greek debt. In 2010 a spokesman for Goldman told the UK Telegraph that As a matter of policy, we don't comment on legal or regulatory matters.
Programmer Involved In Trading Code Theft
In 2011, Sergey Aleynikov, a former computer programmer for Goldman Sachs was sentenced to eight years in jail for stealing source codes from the firm. The computer code that Aleynikov stole had earned Goldman $300 million in 2009 said the New York Times. I never meant to cause Goldman any harm, Aleynikov said in court. His conviction was thrown out in Feb. 2012.
California Bonds
In 2008 the firm had urged big clients to invest in bets against California bonds. A strange twist is that Goldman Sachs had collected millions of dollars in fees after helping California sell some of those same bonds, wrote the Los Angeles Times. According to FierceFinance.com, California believed that the firm could have informed them of their doings first.
Inside Trading
In 1986 David Brown, vice president of Goldman Sachs mortgage securities department was caught for his involvement in an insider trading scandal with Dennis B. Levine, a former executive at Drexel Burnham Lambert. Levine made a reported $12.6 million through the illegal profits. A spokesperson for Goldman Sachs said that they were shocked and dismayed at this development. Neither he (Brown) nor the SEC has informed us of the nature of the investigation except that it is allegedly related to the SEC's recent proceedings against individuals at other firms on insider trading activities.
The New York Times SEC Insider trading Goldman sachs


If you aren't a member of the Cisco Customer Connection program you're missing out! In addition to regular online NDA briefings, we also conduct exclusive roadmap sessions at Cisco Live events. Coming up at Cisco Live Barcelona we've got a full line up. So join the program today and read on if you are attending Cisco Live Barcelona.
If you are attending the event, check out the sessions below. This is a chance to hear about product innovations, have discussion time directly with the Cisco product teams, and meet your fellow CCP members.
This NDA session provides an overview strategy and roadmap for the Cisco Catalyst 9000 family of switches. An open Q&A will be conducted at the conclusion of the presentation.
In this session, we will also explore the future direction and roadmap for SDWAN. The Branch and Enterprise Edge network operations have been experiencing a disruptive change with the advent of software-defined WAN, particularly as application hosting is increasingly moving to the cloud. One of the design goals behind Cisco SD-WAN solution has been to enable a holistic experience, starting with the user access network - through an optimized WAN traffic path - and going all the way toward the application end.
This session covers innovations in wireless. We will discuss the Catalyst 9800 Wireless Controller, the new DNA Spaces as well as cover Cisco's strategy with the emerging Wi-Fi standard 802.11ax.


text:
"""## Casting

TensorFlow provides several operations that you can use to cast tensor data
types in your graph.

@@string_to_number
@@to_double
@@to_float
@@to_bfloat16
@@to_int32
@@to_int64
@@cast

## Shapes and Shaping

TensorFlow provides several operations that you can use to determine the shape
of a tensor and change the shape of a tensor.

@@shape
@@size
@@rank
@@reshape
@@squeeze
@@expand_dims

## Slicing and Joining

TensorFlow provides several operations to slice or extract parts of a tensor,
or join multiple tensors together.

@@slice
@@split
@@tile
@@pad
@@concat
@@pack
@@unpack
@@reverse_sequence
@@reverse
@@transpose
@@gather
@@dynamic_partition
@@dynamic_stitch
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import tensorflow.python.platform
import numpy as np

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import common_shapes
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
# pylint: disable=wildcard-import
# 'Constant' gets imported in the module 'array_ops'.
from tensorflow.python.ops.constant_op import constant
from tensorflow.python.ops.gen_array_ops import *

# We override the 'slice' for the "slice" op, so we keep python's
# existing 'slice' for later use in this module.
_baseslice = slice

# Aliases for some automatically-generated names.
listdiff = gen_array_ops.list_diff

# pylint: disable=undefined-variable,protected-access
def _SliceHelper(tensor, slice_spec):
  """Overload for Tensor.__getitem__.

  Currently the size of the slice must be statically known in each dimension,
  i.e. the "stop" of the slice must not be omitted.

  TODO(mrry): Support slices where the sizes are not specified.
  TODO(mrry): Support negative indices in slices with numpy/Python semantics.

  Args:
    tensor: An ops.Tensor object.
    slice_spec: The arguments to Tensor.__getitem__.

  Returns:
    The appropriate slice of "tensor", based on "slice_spec".

  Raises:
    ValueError: If a slice range is negative size.
    TypeError: If the slice indices aren't int, slice, or Ellipsis.
  """
  if not isinstance(slice_spec, (list, tuple)):
    slice_spec = [slice_spec]
  indices = []
  sizes = []
  squeeze_dims = []
  for dim, s in enumerate(slice_spec):
    if isinstance(s, int):
      if s < 0:
        raise NotImplementedError("Negative indices are currently unsupported")
      indices.append(s)
      sizes.append(1)
      squeeze_dims.append(dim)
    elif isinstance(s, _baseslice):
      if s.step not in (None, 1):
        raise NotImplementedError(
            "Steps other than 1 are not currently supported")
      start = s.start if s.start is not None else 0
      if start < 0:
        raise NotImplementedError(
            "Negative start indices are not currently supported")
      indices.append(start)
      if s.stop is not None and s.stop < 0:
        raise NotImplementedError(
            "Negative stop indices are not currently supported")
      # NOTE(mrry): If the stop is not specified, Python substitutes
      #   sys.maxsize, which is typically (2 ** 63) - 1. Since Slice currently
      #   supports signed DT_INT32 arguments, we use -1 to specify that all
      #   elements should be captured.
      if s.stop is None or s.stop == sys.maxsize:
        sizes.append(-1)
      else:
        if start > s.stop:
          raise ValueError("Stop must be at least start")
        sizes.append(s.stop - start)
    elif s is Ellipsis:
      raise NotImplementedError("Ellipsis is not currently supported")
    else:
      raise TypeError("Bad slice index %s of type %s" % (s, type(s)))
  sliced = slice(tensor, indices, sizes)
  if squeeze_dims:
    return squeeze(sliced, squeeze_dims=squeeze_dims)
  else:
    return sliced

def slice(input_, begin, size, name=None):
  """Extracts a slice from a tensor.

  This operation extracts a slice of size `size` from a tensor `input` starting
  at the location specified by `begin`. The slice `size` is represented as a
  tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
  of `input` that you want to slice. The starting location (`begin`) for the
  slice is represented as an offset in each dimension of `input`. In other
  words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
  want to slice from.

  `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
  all remaining elements in dimension i are included in the
  slice. In other words, this is equivalent to setting:

  `size[i] = input.dim_size(i) - begin[i]`

  This operation requires that:

  `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`

  For example:

  ```
  # 'input' is [[[1, 1, 1], [2, 2, 2]],
  #             [[3, 3, 3], [4, 4, 4]],
  #             [[5, 5, 5], [6, 6, 6]]]
  tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
  tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
                                              [4, 4, 4]]]
  tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
                                             [[5, 5, 5]]]
  ```

  Args:
    input_: A `Tensor`.
    begin: An `int32` or `int64` `Tensor`.
    size: An `int32` or `int64` `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` the same type as `input`.
  """
  return gen_array_ops._slice(input_, begin, size, name=name)

ops.Tensor._override_operator("__getitem__", _SliceHelper)

def pack(values, name="pack"):
  """Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.

  Packs tensors in `values` into a tensor with rank one higher than each tensor
  in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
  `output[i, ...] = values[i][...]`.

  This is the opposite of unpack.  The numpy equivalent is

      tf.pack([x, y, z]) = np.asarray([x, y, z])

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    name: A name for this operation (optional).

  Returns:
    output: A packed `Tensor` with the same type as `values`.
  """
  return gen_array_ops._pack(values, name=name)

def unpack(value, num=None, name="unpack"):
  """Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.

  Unpacks `num` tensors from `value` along the first dimension.
  If `num` is not specified (the default), it is inferred from `value`'s shape.
  If `value.shape[0]` is not known, `ValueError` is raised.

  The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
  `output` has shape `value.shape[1:]`.

  This is the opposite of pack.  The numpy equivalent is

      tf.unpack(x, n) = list(x)

  Args:
    value: A rank `R > 0` `Tensor` to be unpacked.
    num: An `int`. The first dimension of value. Automatically inferred if
      `None` (the default).
    name: A name for the operation (optional).

  Returns:
    The list of `Tensor` objects unpacked from `value`.

  Raises:
    ValueError: If `num` is unspecified and cannot be inferred.
  """
  if num is None:
    value = ops.convert_to_tensor(value)
    shape = value.get_shape()
    num = shape[0].value
    if num is None:
      raise ValueError("Cannot infer num from shape %s" % shape)
  return gen_array_ops._unpack(value, num=num, name=name)

def concat(concat_dim, values, name="concat"):
  """Concatenates tensors along one dimension.

  Concatenates the list of tensors `values` along dimension `concat_dim`.  If
  `values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn]`, the concatenated
  result has shape

      [D0, D1, ... Rconcat_dim, ...Dn]

  where

      Rconcat_dim = sum(Dconcat_dim(i))

  That is, the data from the input tensors is joined along the `concat_dim`
  dimension.

  The number of dimensions of the input tensors must match, and all dimensions
  except `concat_dim` must be equal.

  For example:

  ```python
  t1 = [[1, 2, 3], [4, 5, 6]]
  t2 = [[7, 8, 9], [10, 11, 12]]
  tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
  tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]

  # tensor t3 with shape [2, 3]
  # tensor t4 with shape [2, 3]
  tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
  tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
  ```

  Args:
    concat_dim: 0-D `int32` `Tensor`.  Dimension along which to concatenate.
    values: A list of `Tensor` objects or a single `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` resulting from concatenation of the input tensors.
  """
  if not isinstance(values, (list, tuple)):
    values = [values]
  # TODO(mrry): Change to return values?
  if len(values) == 1:  # Degenerate case of one tensor.
    return identity(values[0], name=name)
  return gen_array_ops._concat(concat_dim=concat_dim,
                               values=values,
                               name=name)

@ops.RegisterShape("Pack")
def _PackShape(op):
  input_shape = op.inputs[0].get_shape()
  for inp in op.inputs[1:]:
    input_shape = input_shape.merge_with(inp.get_shape())
  return [tensor_shape.TensorShape([len(op.inputs)]).concatenate(input_shape)]

@ops.RegisterShape("Unpack")
def _UnpackShape(op):
  input_shape = op.inputs[0].get_shape()
  return [input_shape[1:]] * op.get_attr("num")

@ops.RegisterShape("Concat")
def _ConcatShape(op):
  concat_dim = tensor_util.constant_value(op.inputs[0])
  if concat_dim is None:
    # Return an unknown shape with the same rank as the inputs, or an
    # unknown rank if no input's rank is known.
    rank = None
    for value in op.inputs[1:]:
      if rank is not None:
        value.get_shape().assert_has_rank(rank)
      else:
        rank = value.get_shape().ndims
    if rank == 0:
      raise ValueError("Can't concatenate scalars (use tf.pack instead)")
    return [tensor_shape.unknown_shape(ndims=rank)]

  else:
    # Merge all the non-concat dims, and sum the concat dim to make an
    # output shape.
    concat_dim = int(concat_dim)
    output_shape = op.inputs[1].get_shape()
    for value in op.inputs[2:]:
      value_shape = value.get_shape()
      if value_shape.ndims is not None and concat_dim >= value_shape.ndims:
        raise ValueError("concat_dim is out of range (values rank = %d)" %
                         value_shape.ndims)
      before = output_shape[:concat_dim].merge_with(value_shape[:concat_dim])
      at = output_shape[concat_dim] + value_shape[concat_dim]
      after = output_shape[
          concat_dim + 1:].merge_with(value_shape[concat_dim + 1:])
      output_shape = before.concatenate(at).concatenate(after)
    return [output_shape]

@ops.RegisterShape("ConcatOffset")
def _ConcatOffsetShape(op):
  return [x.get_shape() for x in op.inputs[1:]]

def sparse_mask(a, mask_indices, name=None):
  """Masks elements of `IndexedSlices`.

  Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
  contains a subset of the slices of `a`. Only the slices at indices specified
  in `mask_indices` are returned.

  This is useful when you need to extract a subset of slices in an
  `IndexedSlices` object.

  For example:

  ```python
  # `a` contains slices at indices [12, 26, 37, 45] from a large tensor
  # with shape [1000, 10]
  a.indices => [12, 26, 37, 45]
  tf.shape(a.values) => [4, 10]

  # `b` will be the subset of `a` slices at its second and third indices, so
  # we want to mask of its first and last indices (which are at absolute
  # indices 12, 45)
  b = tf.sparse_mask(a, [12, 45])

  b.indices => [26, 37]
  tf.shape(b.values) => [2, 10]

  ```

  Args:
    * `a`: An `IndexedSlices` instance.
    * `mask_indices`: Indices of elements to mask.
    * `name`: A name for the operation (optional).

  Returns:
    The masked `IndexedSlices` instance.
  """
  with ops.op_scope([a, mask_indices], name, "sparse_mask") as name:
    indices = a.indices
    out_indices, to_gather = listdiff(indices, mask_indices)
    out_values = gather(a.values, to_gather, name=name)
    return ops.IndexedSlices(out_values, out_indices, a.dense_shape)

def split(split_dim, num_split, value, name="split"):
  """Splits a tensor into `num_split` tensors along one dimension.

  Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
  Requires that `num_split` evenly divide `value.shape[split_dim]`.

  For example:

  ```python
  # 'value' is a tensor with shape [5, 30]
  # Split 'value' into 3 tensors along dimension 1
  split0, split1, split2 = tf.split(1, 3, value)
  tf.shape(split0) ==> [5, 10]
  ```

  Args:
    split_dim: A 0-D `int32` `Tensor`. The dimension along which to split.
      Must be in the range `[0, rank(value))`.
    num_split: A Python integer. The number of ways to split.
    value: The `Tensor` to split.
    name: A name for the operation (optional).

  Returns:
    `num_split` `Tensor` objects resulting from splitting `value`.
  """
  return gen_array_ops._split(split_dim=split_dim,
                              num_split=num_split,
                              value=value,
                              name=name)

@ops.RegisterShape("Reverse")
def _ReverseShape(op):
  dims_shape = op.inputs[1].get_shape().with_rank(1)
  input_shape = op.inputs[0].get_shape().with_rank(dims_shape[0])
  if input_shape.ndims is not None and input_shape.ndims > 8:
    raise ValueError(
        "tf.reverse() does not work on tensors with more than 8 dimensions")
  return [input_shape]

def transpose(a, perm=None, name="transpose"):
  """Transposes `a`. Permutes the dimensions according to `perm`.

  The returned tensor's dimension i will correspond to the input dimension
  `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
  the rank of the input tensor. Hence by default, this operation performs a
  regular matrix transpose on 2-D input Tensors.

  For example:

  ```python
  # 'x' is [[1 2 3]
  #         [4 5 6]]
  tf.transpose(x) ==> [[1 4]
                       [2 5]
                       [3 6]]

  # Equivalently
  tf.transpose(x, perm=[1, 0]) ==> [[1 4]
                                    [2 5]
                                    [3 6]]

  # 'perm' is more useful for n-dimensional tensors, for n > 2
  # 'x' is   [[[1  2  3]
  #            [4  5  6]]
  #           [[7  8  9]
  #            [10 11 12]]]
  # Take the transpose of the matrices in dimension-0
  tf.transpose(b, perm=[0, 2, 1]) ==> [[[1  4]
                                        [2  5]
                                        [3  6]]

                                       [[7 10]
                                        [8 11]
                                        [9 12]]]
  ```

  Args:
    a: A `Tensor`.
    perm: A permutation of the dimensions of `a`.
    name: A name for the operation (optional).

  Returns:
    A transposed `Tensor`.
  """
  with ops.op_scope([a], name, "transpose") as name:
    if perm is None:
      dims = gen_math_ops._range(0, gen_array_ops.rank(a), 1)
      perm = gen_array_ops.reverse(dims, [True])
      ret = gen_array_ops.transpose(a, perm, name=name)
      # NOTE(mrry): Setting the shape explicitly because
      #   reverse is not handled by the shape function.
      input_shape = ret.op.inputs[0].get_shape().dims
      if input_shape is not None:
        ret.set_shape(input_shape[::-1])
    else:
      ret = gen_array_ops.transpose(a, perm, name=name)
    return ret

def zeros(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to zero.

  This operation returns a tensor of type `dtype` with shape `shape` and
  all elements set to zero.

  For example:

  ```python
  tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
  ```

  Args:
    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to zero.
  """
  with ops.op_scope([shape], name, "zeros") as name:
    if isinstance(shape, list):
      output = constant(0, shape=shape, dtype=dtype, name=name)
    else:
      shape = ops.convert_to_tensor(shape, name="shape")
      output = fill(shape, constant(0, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
  return output

def zeros_like(tensor, dtype=None, name=None):
  """Creates a tensor with all elements set to zero.

  Given a single tensor (`tensor`), this operation returns a tensor of the
  same type and shape as `tensor` with all elements set to zero. Optionally,
  you can use `dtype` to specify a new type for the returned tensor.

  For example:

  ```python
  # 'tensor' is [[1, 2, 3], [4, 5, 6]]
  tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
  ```

  Args:
    tensor: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
    `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to zero.
  """
  with ops.op_scope([tensor], name, "zeros_like") as name:
    tensor = ops.convert_to_tensor(tensor, name="tensor")
    zeros_shape = shape(tensor)
    if dtype is None:
      dtype = tensor.dtype
    ret = zeros(zeros_shape, dtype=dtype, name=name)
    ret.set_shape(tensor.get_shape())
    return ret

def ones_like(tensor, dtype=None, name=None):
  """Creates a tensor with all elements set to 1.

  Given a single tensor (`tensor`), this operation returns a tensor of the same
  type and shape as `tensor` with all elements set to 1. Optionally, you can
  specify a new type (`dtype`) for the returned tensor.

  For example:

  ```python
  # 'tensor' is [[1, 2, 3], [4, 5, 6]]
  tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
  ```

  Args:
    tensor: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
    `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to 1.
  """
  with ops.op_scope([tensor], name, "ones_like") as name:
    tensor = ops.convert_to_tensor(tensor, name="tensor")
    ones_shape = shape(tensor)
    if dtype is None:
      dtype = tensor.dtype
    ret = ones(ones_shape, dtype=dtype, name=name)
    ret.set_shape(tensor.get_shape())
    return ret

def zeros_initializer(shape, dtype=dtypes.float32):
  """An adaptor for zeros() to match the Initializer spec."""
  return zeros(shape, dtype)

def ones(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to 1.

  This operation returns a tensor of type `dtype` with shape `shape` and all
  elements set to 1.

  For example:

  ```python
  tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
  ```

  Args:
    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to 1.
  """
  with ops.op_scope([shape], name, "ones") as name:
    if isinstance(shape, list):
      output = constant(1, shape=shape, dtype=dtype, name=name)
    else:
      shape = ops.convert_to_tensor(shape, name="shape")
      output = fill(shape, constant(1, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
  return output

def placeholder(dtype, shape=None, name=None):
  """Inserts a placeholder for a tensor that will be always fed.

  **Important**: This tensor will produce an error if evaluated. Its value must
  be fed using the `feed_dict` optional argument to `Session.run()`,
  `Tensor.eval()`, or `Operation.run()`.

  For example:

  ```python
  x = tf.placeholder(tf.float32, shape=(1024, 1024))
  y = tf.matmul(x, x)

  with tf.Session() as sess:
    print(sess.run(y))  # ERROR: will fail because x was not fed.

    rand_array = np.random.rand(1024, 1024)
    print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
  ```

  Args:
    dtype: The type of elements in the tensor to be fed.
    shape: The shape of the tensor to be fed (optional). If the shape is not
      specified, you can feed a tensor of any shape.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` that may be used as a handle for feeding a value, but not
    evaluated directly.
  """
  shape = tensor_shape.as_shape(shape)
  if shape.is_fully_defined():
    dim_list = shape.as_list()
  else:
    dim_list = []
  ret = gen_array_ops._placeholder(
      dtype=dtype,
      shape=dim_list,
      name=name)
  ret.set_shape(shape)
  return ret

@ops.RegisterShape("Placeholder")
def _PlaceholderShape(op):
  given_shape = tensor_util.TensorShapeProtoToList(op.get_attr("shape"))
  if given_shape:
    return [tensor_shape.TensorShape(given_shape)]
  else:
    return [tensor_shape.unknown_shape()]

@ops.RegisterShape("CheckNumerics")
@ops.RegisterShape("Identity")
@ops.RegisterShape("RefIdentity")
@ops.RegisterShape("StopGradient")
def _UnchangedShape(op):
  return [op.inputs[0].get_shape()]

@ops.RegisterShape("Rank")
@ops.RegisterShape("Size")
def _ScalarShape(unused_op):
  return [tensor_shape.scalar()]

@ops.RegisterShape("Slice")
def _SliceShape(op):
  """Shape function for array_ops.slice."""
  input_shape = op.inputs[0].get_shape()
  begin_shape = op.inputs[1].get_shape().with_rank(1)
  sizes_shape = op.inputs[2].get_shape().with_rank(1)
  ndims = begin_shape.merge_with(sizes_shape)[0].value
  if ndims is not None:
    input_shape.assert_has_rank(ndims)
  begin_value = tensor_util.constant_value(op.inputs[1])
  sizes_value = tensor_util.constant_value(op.inputs[2])
  if sizes_value is not None:
    returned_dims = []
    for i, slice_size in enumerate(sizes_value.ravel()):
      if slice_size != -1:
        returned_dims.append(slice_size)
      elif begin_value is not None:
        returned_dims.append(input_shape[i] - begin_value[i])
      else:
        returned_dims.append(None)
    return [tensor_shape.TensorShape(returned_dims)]
  else:
    if input_shape.ndims is not None:
      return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
    elif ndims is not None:
      return [tensor_shape.unknown_shape(ndims=ndims)]
    else:
      return [tensor_shape.unknown_shape()]

@ops.RegisterShape("Gather")
def _GatherShape(op):
  """Shape function for array_ops.gather."""
  params_shape = op.inputs[0].get_shape()
  indices_shape = op.inputs[1].get_shape()
  return [indices_shape.concatenate(params_shape[1:])]

@ops.RegisterShape("Unique")
def _UniqueShape(op):
  """Shape function for array_ops.Unique."""
  # The output is a vector with data-dependent length.
  input_shape = op.inputs[0].get_shape()
  input_shape.assert_has_rank(1)
  return [tensor_shape.vector(None), input_shape]

@ops.RegisterShape("UniqueWithCounts")
def _UniqueWithCountsShape(op):
  """Shape function for array_ops.Unique."""
  # The output is a vector with data-dependent length.
  input_shape = op.inputs[0].get_shape()
  input_shape.assert_has_rank(1)
  return [tensor_shape.vector(None), input_shape, tensor_shape.vector(None)]

@ops.RegisterShape("Diag")
def _DiagShape(op):
  """Shape function for array_ops.diag.

  This op has one input (of rank k <= 3), and one output (of rank 2k),
  where the shape of the output is the concatenation of the input
  shape with itself.

  Args:
    op: A Diag Operation.

  Returns:
    A single-element list containing the shape of the output.
  """
  input_shape = op.inputs[0].get_shape().with_rank_at_most(3)
  return [input_shape.concatenate(input_shape)]

@ops.RegisterShape("ExpandDims")
def _ExpandDimsShape(op):
  """Determine shape for expand op's output tensor.

  Args:
    op: Operation for which to determine shape.
        op.inputs[0] is the input tensor.
        op.inputs[1] is the dimension in which to expand.
  Returns:
    Shape of op's output tensor.
  Raises:
    ValueError: If dim is outside of [-rank - 1, rank], where rank is the number
        of dimensions in the input tensor.
  """
  input_shape = op.inputs[0].get_shape()
  if input_shape.dims is None:
    return [tensor_shape.unknown_shape()]
  dim = tensor_util.constant_value(op.inputs[1])
  input_ndims = input_shape.ndims
  if dim < -input_ndims - 1 or dim > input_ndims:
    raise ValueError(
        "dim %d not in [%d, %d]." % (dim, -input_ndims, input_ndims))
  if dim < 0:
    dim += (input_ndims + 1)
  result_shape = list(input_shape.dims)
  result_shape.insert(dim, 1)
  return [tensor_shape.TensorShape(result_shape)]

@ops.RegisterShape("Squeeze")
def _SqueezeShape(op):
  """Determine shape for squeeze op's output tensor.

  Args:
    op: Operation for which to determine shape.
  Returns:
    Shape of op's output tensor.
  Raises:
    ValueError: if squeeze_dims includes a dimension outside of [-rank, rank),
        where rank is the number of dimensions in the input tensor. Or, if
        squeeze_dims includes a dimension for which input shape has a value
        not equal to 1.
  """
  input_shape = op.inputs[0].get_shape()
  if input_shape.dims is None:
    return [tensor_shape.unknown_shape()]

  squeeze_dims = op.get_attr("squeeze_dims") or []
  wrapped_squeeze_dims = []
  input_ndims = input_shape.ndims
  for i, squeeze_dim in enumerate(squeeze_dims):
    if squeeze_dim < -input_ndims or squeeze_dim >= input_ndims:
      raise ValueError(
          "squeeze_dims[%d]=%d not in [%d, %d)." % (
              i, squeeze_dim, -input_ndims, input_ndims))
    if squeeze_dim < 0:
      squeeze_dim += input_ndims
    wrapped_squeeze_dims.append(squeeze_dim)

  result_shape = []
  for i, dim in enumerate([d.value for d in input_shape.dims]):
    is_explicit_match = i in wrapped_squeeze_dims
    if is_explicit_match or not wrapped_squeeze_dims:
      if dim is None:
        return [tensor_shape.unknown_shape()]
      if dim != 1:
        if is_explicit_match:
          raise ValueError(
              "Can not squeeze dim[%d], expected a dimension of 1, got %d." % (
                  i, dim))
        result_shape.append(dim)
    else:
      result_shape.append(dim)
  return [tensor_shape.TensorShape(result_shape)]

@ops.RegisterShape("Reshape")
def _ReshapeShape(op):
  """Shape function for Reshape op."""
  input_shape = op.inputs[0].get_shape()
  if input_shape.ndims is not None:
    num_elements = tensor_shape.Dimension(1)
    for dim in input_shape.dims:
      num_elements *= dim
  else:
    num_elements = tensor_shape.Dimension(None)
  new_shape_shape = op.inputs[1].get_shape().with_rank(1)
  new_shape = tensor_util.constant_value(op.inputs[1])
  if new_shape is None:
    # Attempt to infer the rank of the output from the length of
    # new_shape.
    return [tensor_shape.unknown_shape(ndims=new_shape_shape[0].value)]
  new_shape = np.reshape(new_shape, -1).tolist()
  if -1 not in new_shape:
    # The new shape is fully defined.
    if (num_elements.value is not None
        and num_elements.value != np.prod(new_shape)):
      raise ValueError(
          "Cannot reshape a tensor with %d elements to shape %s (%d elements)"
          % (num_elements.value, new_shape, np.prod(new_shape)))
    return [tensor_shape.TensorShape(new_shape)]
  elif num_elements.value is not None:
    # We know the number of elements, so we can calculate the missing
    # dimension in the new_shape.
    known_elements = 1
    unknown_index = None
    for i, dim in enumerate(new_shape):
      if dim == -1:
        unknown_index = i
      else:
        known_elements *= dim
    if known_elements == 0:
      raise ValueError("cannot infer the missing input size for "
                       "an empty tensor unless all specified "
                       "input sizes are non-zero")
    if num_elements % known_elements != 0:
      raise ValueError("input has %s elements, which isn't divisible by %d" %
                       (num_elements, known_elements))
    new_shape[unknown_index] = num_elements // known_elements
    return [tensor_shape.TensorShape(new_shape)]
  else:
    # We don't know the input shape, but we know n-1 of the dimensions
    # in the new shape.
    new_shape[new_shape.index(-1)] = None
    return [tensor_shape.TensorShape(new_shape)]

@ops.RegisterShape("BroadcastGradientArgs")
def _BroadcastGradientArgsShape(op):
  """Shape function for the BroadcastGradientArgs op."""
  # TODO(mrry): Implement constant_value for BroadcastGradientArgs?
  op.inputs[0].get_shape().assert_has_rank(1)
  op.inputs[1].get_shape().assert_has_rank(1)
  return [tensor_shape.vector(None), tensor_shape.vector(None)]

@ops.RegisterShape("Fill")
def _FillShape(op):
  """Shape function for the Fill op.

  This op takes a vector of dimensions and a scalar, and produces a
  tensor with the given dimensions.

  Args:
    op: A Fill Operation.

  Returns:
    A single-element list containing the shape of the output.
  """
  dimensions_shape = op.inputs[0].get_shape().with_rank(1)
  op.inputs[1].get_shape().assert_is_compatible_with(tensor_shape.scalar())
  fill_dims = tensor_util.constant_value(op.inputs[0])
  if fill_dims is None:
    # Attempt to infer the rank of the output from the length of
    # dimensions.
    return [tensor_shape.unknown_shape(ndims=dimensions_shape[0].value)]
  else:
    return [tensor_shape.TensorShape(fill_dims.tolist())]

@ops.RegisterShape("InvertPermutation")
def _InvertPermutationShape(op):
  """Shape function for the InvertPermutation op."""
  return [op.inputs[0].get_shape().with_rank(1)]

@ops.RegisterShape("ListDiff")
def _ListDiffShape(op):
  """Shape function for the ListDiff op."""
  op.inputs[0].get_shape().assert_has_rank(1)
  op.inputs[1].get_shape().assert_has_rank(1)
  # TODO(mrry): Indicate that the length falls within an interval?
  return [tensor_shape.vector(None)] * 2

@ops.RegisterShape("Pad")
def _PadShape(op):
  """Shape function for the Pad op.

  This op has two inputs:

  * input: A rank-N tensor.
  * paddings: An N-by-2 matrix, in which the i^th row contains the
    number of padding elements to add before and after `input` in the
    i^th dimension.

  It has one output, which has the same rank as input, and additional
  elements according to the values in paddings.

  Args:
    op: A Pad Operation.

  Returns:
    A single-element list containing the shape of the output.

  Raises:
    ValueError: If the input shapes are incompatible.
  """
  paddings_shape = op.inputs[1].get_shape().with_rank(2)
  input_shape = op.inputs[0].get_shape()
  input_shape = input_shape.with_rank(paddings_shape[0].value)
  paddings_shape = paddings_shape.merge_with(
      tensor_shape.matrix(input_shape.ndims, 2))
  paddings = tensor_util.constant_value(op.inputs[1])
  if paddings is None:
    return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
  else:
    output_dims = []
    for i, dim in enumerate(input_shape.dims):
      if paddings[i, 0] < 0 or paddings[i, 1] < 0:
        raise ValueError("paddings must be non-negative")
      output_dims.append(dim + paddings[i, 0] + paddings[i, 1])
    return [tensor_shape.TensorShape(output_dims)]

@ops.RegisterShape("ReverseSequence")
def _ReverseSequenceShape(op):
  """Shape function for the ReverseSequence op.

  This op has two inputs:

  * input: A rank-N tensor with size B in the 0th dimension.
  * seq_lens: A vector of length B.

  It has one output, with the same size as input.

  Args:
    op: A ReverseSequence Operation.

  Returns:
    A single-element list containing the shape of the output.

  Raises:
    ValueError: If the input shapes are incompatible or seq_dim == batch_dim.
  """
  input_shape = op.inputs[0].get_shape()
  seq_lens_shape = op.inputs[1].get_shape().with_rank(1)
  seq_dim = op.get_attr("seq_dim")
  batch_dim = op.get_attr("batch_dim")
  if batch_dim >= input_shape.ndims:
    raise ValueError("batch_dim must be < input.dims() (%d vs %d)" %
                     (batch_dim, input_shape.ndims))
  if seq_dim >= input_shape.ndims:
    raise ValueError("seq_dim must be < input.dims() (%d vs %d)" %
                     (seq_dim, input_shape.ndims))
  batch_size = input_shape[batch_dim].merge_with(seq_lens_shape[0])
  input_shape = tensor_shape.TensorShape([
      value if ix != batch_dim else batch_size
      for ix, value in enumerate(input_shape)])
  return [input_shape]

@ops.RegisterShape("Shape")
@ops.RegisterShape("ShapeN")
def _ShapeNShape(op):
  """Shape function for the Shape/ShapeN op."""
  return [tensor_shape.vector(x.get_shape().ndims) for x in op.inputs]

@ops.RegisterShape("Transpose")
def _TransposeShape(op):
  """Shape function for the Transpose op.

  This op takes two inputs:

  * input: a rank-N tensor of arbitrary shape.
  * shuffle: a length-N vector.

  Its output is the rank-N tensor computed by permuting the dimensions
  of input according to shuffle.

  Args:
    op: A Transpose op.

  Returns:
    A single-element list containing the shape of the output.

  Raises:
    ValueError: If the shapes of input and shuffle are incompatible.
    IndexError: If shuffle contains an index that is >= the rank of input.
  """
  input_shape = op.inputs[0].get_shape()
  transpose_shape = op.inputs[1].get_shape().merge_with(tensor_shape.vector(
      input_shape.ndims))
  transpose_vec = tensor_util.constant_value(op.inputs[1])
  if transpose_vec is None:
    return [tensor_shape.unknown_shape(ndims=transpose_shape[0].value)]
  else:
    return [tensor_shape.TensorShape([input_shape[i]
                                      for i in transpose_vec.tolist()])]

@ops.RegisterShape("Split")
def _SplitShape(op):
  """Shape function for the Split op."""
  split_dim = tensor_util.constant_value(op.inputs[0])
  num_split = len(op.outputs)
  input_shape = op.inputs[1].get_shape()
  if split_dim is None:
    return [tensor_shape.unknown_shape(ndims=input_shape.ndims)] * num_split
  else:
    split_dim = int(split_dim)
    input_shape = input_shape.with_rank_at_least(split_dim + 1)
    if not (input_shape[split_dim] % num_split).is_compatible_with(0):
      raise ValueError(
          "Number of ways to split should evenly divide the split "
          "dimension but got split_dim %d (size = %d) and num_split %d" %
          (split_dim, input_shape[split_dim].value, num_split))
    prefix = input_shape[:split_dim]
    size_in_split_dim = input_shape[split_dim] // num_split
    suffix = input_shape[split_dim + 1:]
    output_shape = prefix.concatenate(size_in_split_dim).concatenate(suffix)
    return [output_shape] * num_split

@ops.RegisterShape("Tile")
def _TileShape(op):
  """Shape function for the Tile op.

  This op has two inputs:

  * input: A rank-N tensor.
  * multiples: A length-N vector, in which the i^th element contains
    the factor by which `input` will be tiled in the i^th dimension.

  It has one output, which has the same rank as input, and additional
  elements according to the values in multiples

  Args:
    op: A Tile Operation.

  Returns:
    A single-element list containing the shape of the output.
  """
  multiples_shape = op.inputs[1].get_shape().with_rank(1)
  input_shape = op.inputs[0].get_shape().with_rank(multiples_shape[0].value)
  multiples = tensor_util.constant_value(op.inputs[1])
  if multiples is None:
    return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
  else:
    output_dims = []
    multiples = multiples.ravel()
    for i, dim in enumerate(input_shape.dims):
      output_dims.append(dim * multiples[i])
    return [tensor_shape.TensorShape(output_dims)]

@ops.RegisterShape("TileGrad")
def _TileGradShape(op):
  """Shape function for the TileGrad op."""
  multiples_shape = op.inputs[1].get_shape().with_rank(1)
  input_shape = op.inputs[0].get_shape().with_rank(multiples_shape[0])
  multiples = tensor_util.constant_value(op.inputs[1])
  if multiples is None:
    return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
  else:
    output_dims = []
    for i, dim in enumerate(input_shape.dims):
      output_dims.append(dim // multiples[i])
    return [tensor_shape.TensorShape(output_dims)]

@ops.RegisterShape("Where")
def _WhereShape(op):
  """Shape function for the Where op."""
  input_shape = op.inputs[0].get_shape()
  return [tensor_shape.matrix(None, input_shape.ndims)]

@ops.RegisterShape("ZerosLike")
def _ZerosLikeShape(op):
  """Shape function for the ZerosLike op."""
  return [op.inputs[0].get_shape()]

def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
  """Computes the Levenshtein distance between sequences.

  This operation takes variable-length sequences (`hypothesis` and `truth`),
  each provided as a `SparseTensor`, and computes the Levenshtein distance.
  You can normalize the edit distance by length of `truth` by setting
  `normalize` to true.

  For example, given the following input:

  ```python
  # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
  #   (0,0) = ["a"]
  #   (1,0) = ["b"]
  hypothesis = tf.SparseTensor(
      [[0, 0, 0],
       [1, 0, 0]],
      ["a", "b"]
      (2, 1, 1))

  # 'truth' is a tensor of shape `[2, 2]` with variable-length values:
  #   (0,0) = []
  #   (0,1) = ["a"]
  #   (1,0) = ["b", "c"]
  #   (1,1) = ["a"]
  truth = tf.SparseTensor(
      [[0, 1, 0],
       [1, 0, 0],
       [1, 0, 1],
       [1, 1, 0]]
      ["a", "b", "c", "a"],
      (2, 2, 2))

  normalize = True
  ```

  This operation would return the following:

  ```python
  # 'output' is a tensor of shape `[2, 2]` with edit distances normalized
  # by 'truth' lengths.
  output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
             [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
  ```

  Args:
    hypothesis: A `SparseTensor` containing hypothesis sequences.
    truth: A `SparseTensor` containing truth sequences.
    normalize: A `bool`. If `True`, normalizes the Levenshtein distance by
      length of `truth.`
    name: A name for the operation (optional).

  Returns:
    A dense `Tensor` with rank `R - 1`, where R is the rank of the
    `SparseTensor` inputs `hypothesis` and `truth`.

  Raises:
    TypeError: If either `hypothesis` or `truth` are not a `SparseTensor`.
  """
  if not isinstance(hypothesis, ops.SparseTensor):
    raise TypeError("Hypothesis must be a SparseTensor")
  if not isinstance(truth, ops.SparseTensor):
    raise TypeError("Truth must be a SparseTensor")

  return gen_array_ops._edit_distance(hypothesis.indices,
                                      hypothesis.values,
                                      hypothesis.shape,
                                      truth.indices,
                                      truth.values,
                                      truth.shape,
                                      normalize=normalize,
                                      name=name)

@ops.RegisterShape("EditDistance")
def _EditDistanceShape(op):
  """Shape function for the EditDistance op."""
  hypothesis_shape = tensor_util.constant_value(op.inputs[2])
  truth_shape = tensor_util.constant_value(op.inputs[5])
  if hypothesis_shape is not None and truth_shape is not None:
    if len(hypothesis_shape) != len(truth_shape):
      raise ValueError(
          "Inconsistent ranks in hypothesis and truth.  Saw shapes: %s and %s" %
          (str(hypothesis_shape), str(truth_shape)))
    return [tensor_shape.TensorShape(
        [max(h, t) for h, t in zip(hypothesis_shape[:-1], truth_shape[:-1])])]

  return [tensor_shape.unknown_shape()]

# The remaining ops do not change the shape of their inputs.
@ops.RegisterShape("Quantize")
@ops.RegisterShape("Dequantize")
def _QuantizeDequantizeShape(op):
  unused_min_range = op.inputs[1].get_shape().merge_with(tensor_shape.scalar())
  unused_max_range = op.inputs[2].get_shape().merge_with(tensor_shape.scalar())
  return common_shapes.unchanged_shape(op)

redpajama_set_name: RedPajamaGithub


The Resource Forum of the Islamic Movement in Nigeria has held its 9th Annual Sallah Feast in Bauchi, Nigeria, on Saturday 21st Zul hijjah, 1439 (1st September, 2018) with many dignities in attendance.
Amongst those present include Prof Dahiru Yahya as the special guest speaker, Shaikh Adamu tsoho, Shaikh Ahmad Yashi, Shakih Dawud Azare, Dr Nuradeen Azare, Prof Isa Mshelgaru, Mal Sadiqu Inuwa a Muslim cleric in Bauchi, Alhaji Musa Santuraki Pali Chairman Ashabul Kahfi Bauchi, Hon Hafiz Mai Auduga , Rev. G G Kumbe Vice Chairman of CAN Bauchi, M S apostle, S O Fagbemi, Rev. Masoyi J Dadi, Evang. Emmanuel Okere, Alh Mi'ka'il Ajiyan Bura, Malama Suwaiba Muhammad, Shaikh Dan'azumi Musa Tafawa Balewa, Deputy Chairman Council of Ulama in Nigeria, Bauchi chapter, Alhaji Sani Al'amin a Politician etc.
In his introductory speech, Prof Abdullahi Danladi the Coordinator of the Forum mentioned that sallah feast was initiated nine years ago to serve as avenue for a get together, where followers of Shaikh Ibraheem Zakzaky (H) who were together at one time in different higher institutions of higher learning and now living in different parts of the country and the rest of the world could come together.
More so to serve as an avenue for interaction of like minds who were once together in different higher institutions and working together to bring positive change in the country and to uphold supreme authority of Allah (T) for the progress of the Nation.
Likewise the Feast serves as avenue for mutual understanding and unity amongst people irrespective of differences in ideology, religion, ethnicity etc. He also spoke of the significance of Eidul Ghadeer where the Noble Prophet (SAWA) appointed Imam Aliy (AS) as his successor onto whom the Muslims should owe allegiance for guidance. This very path, he siad, is the path which Shaikh Ibraheem Zakzaky (H) has been calling people to, which has lead to those in the helms of affairs in Nigeria to carry out a historic massacre of the followers of the Sheikh in December, 2015 in Zaria while the Shaikh, his wife Malama Zeenatudeen and many of his followers aare being held in detention despite a Federal High court order, ordering his release since 2016.
The Special Guest Speaker, Prof Dahiru Yahya a renowned Historian in his speech at the occasion on the topic "Nigeria Nation and the Way Out", reviewed the history of various struggles that were intended at seeing Nigerian Nation moved out of the problem they were in, one of which was in the Nigerian constitutional assembly in which he was a member. At that time he called for Kano to move out of the Nigerian nation, that after the debates it was agreed to maintain status quo, but no progressive change was seen. He added that a revolutionary ideology was heard from a higher institution with a call to Islam lead by Shaikh Ibraheem Zakzaky (H). Today this movement has grown with large followership within Nigeria, neighboring countries and infact different places of the World moving towards that very path shown by the Noble Prophet (SAWA). The population classified into Muslims and that are understanding Islam and will become Muslims.
He also reiterated this very movement led by Shaikh Zakzaky (H) is being oppressed, molested in different forms by tyrants and oppressors, in the name of being Shia Movement. He also explained that the path Shaikh Zakzaky (H) is calling people to is not different from that of the Noble Prophet (SAWA) and his Ahlul Bait (AS). He further said after the Prophet (SAWA) history has documented various attrocities meted on his true and righful followers by those who betrayed his noble path.
Prof Dahiru Yahya called toward upholding justice and unity amongst Muslims irrespective of differences, while they should always try to know themselves better through different means, because it is this very path that will foster them together and protect them from any form of tyranny championed by the world arrogant and oppressors.
Rev G G kumbe the deputy CAN President in Bauchi State, made comment in which he stated how he was pleased and happy when he interacted with followers of Shaikh Zakzaky (H) where he saw them to be peaceful, expressing love and care to different people irrespective of their religion or tribe. He caledl on all to uphold this very attitude of brothers and sisters of the Islamic movement upholding justice and calling for unity.
Shaikh Dan'Azumi Musa, in his remarks also called for unity amongst Muslims expressing even Imam Shafi'I in his books has commended the Shia's in which he sees no reason castigating the Shia's.
More so Hafiz Mai Aduga, Engr Ahmad Yusuf also called for unity for the progress of this great nation.
Shaikh Adamu Tsoho made an opening prayer with recitation of some few verses of our Noble Qur'an from Gwani Karibullah. After feast was served, Shaikh Ahmad Yashi closed this year's feast with closing prayer.


text:
Q: Generating Adjacency List in NetworkX - How to Include Previous Nodes? According to the NetworkX documentation, the function generate_adjlist() generates an adjacency list shown below:
>>> G = nx.lollipop_graph(4, 3)
>>> for line in nx.generate_adjlist(G):
...     print(line)
0 1 2 3
1 2 3
2 3
3 4
4 5
5 6
6

However, I notice that the adjacency list do not include the nodes that were linked previously. For example, if you look at row three (2 3), it does not include 0 even though at row one (0 1 2 3), 0 links to node 2.
How do I include the previous nodes which were omitted?

A: This only applies to undirected graphs. By definition in an adjacency list edges that have already been mentioned do not need to be repeated. When creating a graph, if you were to give over a file that had repeating connections, they would simply be ignored as they are redundant. Your option is to create your own function that lists all the nodes, irrespectively of the fact that they were already mentioned. It would look something like this (code shamelessly modified from the original generate_adjlist code):
def generate_adjlist_with_all_edges():
     for s, nbrs in G.adjacency():
        line = str(s) + delimiter
        for t, data in nbrs.items():
                line += str(t) + delimiter
        yield line[: -len(delimiter)]

G = nx.lollipop_graph(4, 3)
for line in generate_adjlist_with_all_edges(G):
    print(line)
> 0 1 2 3
  1 0 2 3
  2 0 1 3
  3 0 1 2 4
  4 5 3
  5 4 6
  6 5

redpajama_set_name: RedPajamaStackExchange


text:
Didier believes duel with Morocco won't be easy.
France coach Didier Deschamps is confident that meeting Morocco in the next round will be a tough one. Rooster defeated England 2-1 in the quarterfinals of the 2022 football World Cup On Saturday night. Les Bleus will face Morocco in the semi-finals on Wednesday.
"Not many people expected to see in the semi-finals. They surprise everyone. People and they deserve to be there," Deschamps stated when asked about Morocco, their semi-final rivals.
"We have a lot of respect for our competitors. UFABET And they deserve that for coming this far. No one can take that away from them. Let's taste victory tonight against a very good team, England.
"Morocco will be a very difficult team to beat. It's amazing what they've accomplished. They played and beat against what is considered the best team in the world. It's a historic achievement.
"Seeing them didn't surprise me at all. We have seen them put on some great performances over the years. I have to give credit to them."
Deschamps said that "It was a big game. We played against England who are strong physically and technically. It's great for a player who is in the bottom four again. We were fortunate to lose two penalties but we kept the lead with heart and courage.'
Deschamps also said of the semi-final against Morocco: "We will prepare thoroughly for the next game. They just conceded just one goal. Seeing them where they are now is not a surprise."
Tagged with: Arsenal, Aston Villa, Barcelona, Chelsea, Crystal Palace, football, Inter Milan, Juventus, Liverpool, Manchester United, Premier League, PSG, sportnews, sportonline, Spurs, UFABET, West Ham
Game and Sport


On-Demand Password is a tokenless approach to providing two-factor authentication without the requirement for users to carry a physical one-time password generator - a hardware token. When a user is trying to access an application secured by two-factor authentication, the user will receive an one-time password generated by the authentication server and delivered to the user via a specified communication channel such as SMS text message, email or voice call.
T-Pass is an on-demand, one-time password authentication solution that delivers passwords to your phones via Text message, Telephone call, Twitter direct message or Email messages.
GridGo is an on-demand, on-screen security grid that is delivered to the user's logon screen in real time.


text:
Why Nipsey Hussle's Death Set Off a Conspiracy Theory
Ashley Nkadi on the long, destructive history behind the anxieties that give rise to seemingly delusional theories like the one that rose up after Nipsey Hussle's shooting.
By Ashley Nkadi
Photo Illustration by Alicia Tatone
Last Sunday, the 33-year-old Grammy-nominated rapper Ermias Asghedom, otherwise known as Nipsey Hussle, was shot dead in front of one of his Los Angeles clothing stores.
More than a rapper, Nipsey established himself as a community fixture in his native South L.A., where he accrued local esteem by performing charitable acts both strategic (like opening a co-working space and STEM center) and full of heart (such as paying for funerals and giving out shoes to children). But as condolences poured out across social media, one insidious story started bubbling up in various corners of the Internet: a conspiracy theory that the government had plotted Nipsey's death.
Musings on the theory, which were shockingly abundant, hinged on the idea that the government executed Nipsey to silence his upcoming documentary on Dr. Sebi, a Honduran herbalist named Alfredo Bowman who was acquitted of criminal charges in the 1980s for practicing medicine without a license in New York. According to the whisperers, the documentary would purportedly reveal that the cure for HIV/AIDS was kept from society by the powers that be. When Nick Cannon announced that he would finish Nipsey's work, posting photos of Dr. Sebi with notes about Nipsey, it sent the whisperers into a tizzy to "protect" him from Big Pharma, too.
In reality, Dr. Sebi was about as much of a doctor as Dr. Pepper. He claimed he cured patients of HIV/AIDS with an alkaline diet, which he said could regulate the body's pH or acidity levels—food intake can't change the body's blood acidity levels—and was arrested for money laundering several times in 2016. The idea that the government murdered a hip-hop artist to cover up an herbalist documentary is assuredly untrue. But there's something to the anxieties driving it.
The U.S. government has carried out a number of unthinkable—but verifiable—contemptible acts that seem straight out of fiction: poisoning alcohol during Prohibition; covertly bringing Nazi scientists to the United States; the CIA's mind-control experiments. As today's QAnon believers and anti-vaxxers demonstrate, conspiracy theorizing isn't unique to any single American group. But the black community, in particular, has been on the receiving end of real and outrageously pernicious endeavors by the state, and community pillars have been targeted by the government. There are sweeping examples, such as discriminatory mortgage lending called "redlining," which, though now illegal, still shapes our nation's highly segregated housing, neighborhoods, and school districts.
There are also more specific incidents. In 1932, the U.S. Public Health Service launched the "Tuskegee Study of Untreated Syphilis in the Negro Male." Researchers promised hundreds of study participants free treatment, health care, meals, and burial insurance in exchange for their commitment to the program. In actuality, they were not treated at all. Instead, they were monitored by physicians and researchers to see the heights that the untreated illness could reach. The study went on for 40 years, as syphilis decimated the participants.
Around the same time, legal eugenics programs took place in 32 states. The government orchestrated thousands of forced and coerced sterilizations of women who it deemed were unfit to regulate their own reproductive activities. Often, this meant black women, especially in southern states. Unnecessary hysterectomies—sometimes disguised as appendectomies—were performed on black girls as young as 9 years old, without their informed consent, during routine doctor visits, at state institutions, in prisons, and sometimes even at the requests of social workers.
In the 1950s and 1960s, the Federal Bureau of Investigation's COINTELPRO—counterintelligence program—against social justice leaders and organizations was especially egregious. The program surveilled activists and civil rights organizations, including Martin Luther King Jr. and the Black Panther Party, seeking to infiltrate and discredit the civil rights and Black Power movements, as well as their leaders. COINTELPRO employed spies, wiretaps, false media, and falsified correspondence and coordinated with local law enforcement to carry out illegal break-ins, unlawful arrests, assaults, raids, and even executions. Nipsey wouldn't have been the first beloved community pillar targeted by the government. The FBI actually sent a letter to King, urging him to kill himself: "King, there is only one thing left for you to do. You know what it is…You are done. There is but one way out for you. You better take it before your filthy, abnormal fraudulent self is bared to the nation."
It's not over, either. COINTELPRO still monitors black activist groups, including Black Lives Matter. Beyond that, on the local level, city officials and departments have suppressed or lost body-camera and dash-camera footage in instances of fatal police encounters. In Chicago, it took a year for the police department to release footage of Laquan McDonald's death, and only after a journalist filed a Freedom of Information Act request and then took the Chicago Police Department to court. And in New York, police targeted, harassed, and jailed Ramsey Orta, after he filmed Eric Garner's death in a police officer's chokehold and that video went viral.
To the black community, instances of police harassment or coordinated cover-up are so familiar, they're rote—the real lunacy isn't believing that the authorities might enact a grotesque plot, but that authorities have done it so often and egregiously. And yet black people are often treated as if these very real histories are paranoid delusions, rather than the cause of psychic trauma left behind in the collective mind of the black community. We are often slow to seek medical attention, safety, aid, or resources from institutions because we've long been denied.
Conspiracy theories in our community seem plausible because history is marked with actual coordinated abuses against our neighborhoods, our children, and our people. They defy reason but define cruelty, spreading a little madness to us all. And while the specific rumors about Nipsey Hussle and Dr. Sebi are nothing more than wacky conspiracy theories, there is nothing crazy about the very real fears and anxieties from which they arise.
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text:
Q: For a d3js graph with x equal to time since boot (not since epoch), I want better x-axis tick labels In a d3js graph with time on the x-axis, I want to display better tick labels when an ntp reference is not available (and user did not set the date otherwise).  In a device with no ntp server, the timestamps may just be recorded as number of seconds since boot up.  When that is the situation, I don't want to report the graph times as some crazy wall clock time, hh:mm, in 1970 or 1971, which is what I get by default.
One option would be to adjust the timestamps by adding timediff into each timestamp, where:
timediff = client.timenow - server.timenow

symbolic of calling "time(0)" on the client and the server and getting the diff.  That works if the client/browser system has a (good) ntp server (and assuming we know the server time and timestamps are clearly zero-based.  But, if the client also does not have a proper time reference, what should I display?
An option would be to show (days, hours, minutes, seconds) since the device started counting time, perhaps in a format like:
Days HH:MM:SS
  93 12:01:05

with a time axis label like "Elapsed time since boot".  Time axis labels could use just HH:MM:SS until a boundary 24 hour point is hit and then throw in something like "Day 94" at a more major tick mark.
Any suggestions on how to handle this case?  I skimmed D3's time and time interval options but didn't see a solution.  Thanks!


A very well presented detached country cottage with further accommodation attached. The property has great flexibility for potential income or multigenerational living.
The smaller of the properties offers sitting room with fireplace, kitchen and shower room, and on the first floor is a large double bedroom.
Ivy and Rose Cottages could be ideal for residential with holiday accommodation in the self contained cottage, or perhaps extended families.
The property further benefits from double glazed windows and LPG central heating.
This delightful home is situated in the small hamlet of Marsh, which in turn is located within the Blackdown Hills Area of Outstanding Natural Beauty. Marsh has a popular public house, The Flintlock Inn and is just off the A303, therefore having very good road links. The town of Chard is about 5.5 miles to the East and has an extensive range of shops and supermarkets and the market town of Honiton lies about 13 miles to the South West. Honiton has an excellent range of amenities, including a number of interesting antique shops and art gallery together with leisure facilities and a main line rail link to London (Waterloo). The county town of Taunton is about 11 miles to the north with a comprehensive range of shopping facilities, theatre and access to the M5 motorway, via junction 25 and main line rail link to London (Paddington).
To the front is a gravelled area with separated covered entrances to both cottages. The rear garden is mainly laid to lawn with a useful stone outbuilding and a large drive and parking to the side.


Squatty Potty's Unicorn Gold destroys toilet odours!
Great for home or on the go; no need to be embarrassed again!
Unicorn Gold magics away bathroom odours, leaving behind a forest scent!
When you've got to go, you've got to go! But you don't want people to know what you've been up to in there – and normal bathroom sprays are no use, just adding a fake chemical smell on top of the odour. Unicorn Gold is a magical alternative! Just spray a couple of spritzes onto the water before you go and any smells will be trapped. When you flush, the stink is all whisked away and the air is filled with a natural forest scent!
Bergamot and bay leaf essential oils will create a refreshing atmosphere that's a million times nicer to smell than what you've just done! Unicorn Spray contains colloidal gold nanoparticles that neutralise sulphur and bacteria for added odour and germ fighting effects. Keep a bottle at home and one in your bag – it's ideal for use in public toilets!