parameters: (values,
                                 value_range,
                                 nbins=100,
                                 dtype=dtypes.int32,
                                 name=None)
docstring: Bins the given values for use in a histogram.
  
    Given the tensor `values`, this operation returns a rank 1 `Tensor`
    representing the indices of a histogram into which each element
    of `values` would be binned. The bins are equal width and
    determined by the arguments `value_range` and `nbins`.
  
    Args:
      values:  Numeric `Tensor`.
      value_range:  Shape [2] `Tensor` of same `dtype` as `values`.
        values <= value_range[0] will be mapped to hist[0],
        values >= value_range[1] will be mapped to hist[-1].
      nbins:  Scalar `int32 Tensor`.  Number of histogram bins.
      dtype:  dtype for returned histogram.
      name:  A name for this operation (defaults to 'histogram_fixed_width').
  
    Returns:
      A `Tensor` holding the indices of the binned values whose shape matches
      `values`.
  
    Raises:
      TypeError: If any unsupported dtype is provided.
      tf.errors.InvalidArgumentError: If value_range does not
          satisfy value_range[0] < value_range[1].
  
    Examples:
  
    ```python
    # Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
    nbins = 5
    value_range = [0.0, 5.0]
    new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
  
    with tf.compat.v1.get_default_session() as sess:
      indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5)
      variables.global_variables_initializer().run()
      sess.run(indices) # [0, 0, 1, 2, 4, 4]
    ```
docstring_summary: Bins the given values for use in a histogram.
docstring_tokens: Bins the given values for use in a histogram .
function: def histogram_fixed_width_bins(values,
                                 value_range,
                                 nbins=100,
                                 dtype=dtypes.int32,
                                 name=None):
    """Bins the given values for use in a histogram.
  
    Given the tensor `values`, this operation returns a rank 1 `Tensor`
    representing the indices of a histogram into which each element
    of `values` would be binned. The bins are equal width and
    determined by the arguments `value_range` and `nbins`.
  
    Args:
      values:  Numeric `Tensor`.
      value_range:  Shape [2] `Tensor` of same `dtype` as `values`.
        values <= value_range[0] will be mapped to hist[0],
        values >= value_range[1] will be mapped to hist[-1].
      nbins:  Scalar `int32 Tensor`.  Number of histogram bins.
      dtype:  dtype for returned histogram.
      name:  A name for this operation (defaults to 'histogram_fixed_width').
  
    Returns:
      A `Tensor` holding the indices of the binned values whose shape matches
      `values`.
  
    Raises:
      TypeError: If any unsupported dtype is provided.
      tf.errors.InvalidArgumentError: If value_range does not
          satisfy value_range[0] < value_range[1].
  
    Examples:
  
    ```python
    # Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
    nbins = 5
    value_range = [0.0, 5.0]
    new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
  
    with tf.compat.v1.get_default_session() as sess:
      indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5)
      variables.global_variables_initializer().run()
      sess.run(indices) # [0, 0, 1, 2, 4, 4]
    ```
    """
    with ops.name_scope(name, 'histogram_fixed_width_bins',
                        [values, value_range, nbins]):
      values = ops.convert_to_tensor(values, name='values')
      shape = array_ops.shape(values)
  
      values = array_ops.reshape(values, [-1])
      value_range = ops.convert_to_tensor(value_range, name='value_range')
      nbins = ops.convert_to_tensor(nbins, dtype=dtypes.int32, name='nbins')
      nbins_float = math_ops.cast(nbins, values.dtype)
  
      # Map tensor values that fall within value_range to [0, 1].
      scaled_values = math_ops.truediv(
          values - value_range[0],
          value_range[1] - value_range[0],
          name='scaled_values')
  
      # map tensor values within the open interval value_range to {0,.., nbins-1},
      # values outside the open interval will be zero or less, or nbins or more.
      indices = math_ops.floor(nbins_float * scaled_values, name='indices')
  
      # Clip edge cases (e.g. value = value_range[1]) or "outliers."
      indices = math_ops.cast(
          clip_ops.clip_by_value(indices, 0, nbins_float - 1), dtypes.int32)
      return array_ops.reshape(indices, shape)
function_tokens: def histogram_fixed_width_bins ( values , value_range , nbins = 100 , dtype = dtypes . int32 , name = None ) : with ops . name_scope ( name , 'histogram_fixed_width_bins' , [ values , value_range , nbins ] ) : values = ops . convert_to_tensor ( values , name = 'values' ) shape = array_ops . shape ( values ) values = array_ops . reshape ( values , [ - 1 ] ) value_range = ops . convert_to_tensor ( value_range , name = 'value_range' ) nbins = ops . convert_to_tensor ( nbins , dtype = dtypes . int32 , name = 'nbins' ) nbins_float = math_ops . cast ( nbins , values . dtype ) # Map tensor values that fall within value_range to [0, 1]. scaled_values = math_ops . truediv ( values - value_range [ 0 ] , value_range [ 1 ] - value_range [ 0 ] , name = 'scaled_values' ) # map tensor values within the open interval value_range to {0,.., nbins-1}, # values outside the open interval will be zero or less, or nbins or more. indices = math_ops . floor ( nbins_float * scaled_values , name = 'indices' ) # Clip edge cases (e.g. value = value_range[1]) or "outliers." indices = math_ops . cast ( clip_ops . clip_by_value ( indices , 0 , nbins_float - 1 ) , dtypes . int32 ) return array_ops . reshape ( indices , shape )